Machine Learning-Optimized Dual-Band LoRa Elliptical Patch Antenna in LoRa Communication System for Waterborne Microplastic Detection
| dc.contributor.author | Romputtal, Adisak | |
| dc.contributor.author | Phongcharoenpanich, Chuwong | |
| dc.date.accessioned | 2026-08-06T10:54:42Z | |
| dc.date.available | 2026-08-06T10:54:42Z | |
| dc.date.issued | 2026-02-01 | |
| dc.description.abstract | This research proposes a dual-band LoRa elliptical patch antenna for the LoRa communication system to detect waterborne microplastics. The proposed LoRa communication system comprises a LoRa sensor node board and an IoT-LoRa gateway board. The LoRa sensor node board is used to capture microplastic images using a digital camera and collect analog signal data from an 8 × 8 photodiode array which detects the reflected light from microplastic fragments. The data are transmitted using a LoRa elliptical patch antenna in the sensor node board, operating at 0.915 GHz for long-range data transfer. The IoT-LoRa gateway board is used to forward data received from the LoRa sensor node board to a cloud server via the internet, and the stored data are accessible and viewable via a smartphone. In this research, the antenna design is optimized by using machine learning (ML) algorithms, unlike conventional antenna design methods which rely on the manual and iterative process. The ML-optimized dual-band LoRa elliptical patch antenna covers the LoRa, UHF RFID, and ZigBee frequency bands, with an omnidirectional radiation pattern. The measured impedance bandwidths (IBWs) are 8.93% (0.868–0.949 GHz) and 12.69% (2.36–2.68 GHz) for the lower and upper frequency bands, respectively, with the corresponding impedance matching (|S<inf>11</inf>|) of –23.02 dB at 0.907 GHz and −27.27 dB at 2.52 GHz. Two ML-optimized LoRa elliptical patch antennas are subsequently integrated into the LoRa communication system, that is, one on the LoRa sensor node board and other on the IoT-LoRa gateway board. Furthermore, prior to indoor and outdoor experiments, the ML-driven waterborne microplastic detection scheme with the LoRa communication system is trained and tested using camera-captured images and analog signal-converted images from the photodiode array. The ML-driven microplastic detection scheme can classify different types of microplastics in water, achieving an accuracy of 100% for all types of microplastics. The detection scheme is also capable of identifying the presence of microplastics in water, achieving an overall accuracy of 98.5%. The originality of this work lies in the use of ML algorithm to optimize the antenna design and to streamline identification and detection of microplastics in water. | |
| dc.identifier.citation | IEEE Internet of Things Journal, 13(3), 4949-4963, 2026 | |
| dc.identifier.doi | 10.1109/JIOT.2025.3639403 | |
| dc.identifier.issn | 23274662 | |
| dc.identifier.other | 2-s2.0-105023884016 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17884 | |
| dc.source | IEEE Internet of Things Journal | |
| dc.subject | Internet of Things (IoT) | |
| dc.subject | LoRa antenna | |
| dc.subject | LoRa communication system | |
| dc.subject | machine learning (ML) model | |
| dc.subject | microplastic | |
| dc.subject | photodiode array | |
| dc.title | Machine Learning-Optimized Dual-Band LoRa Elliptical Patch Antenna in LoRa Communication System for Waterborne Microplastic Detection | |
| dc.type | Article |